Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Abstract

We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-shot learning. Data-driven deep learning models have achieved remarkable performance and demonstrated capabilities surpassing human experts in many applications. Yet, their inability to exploit domain knowledge leads to serious performance limitations in practical applications. In particular, deep learning systems are exposed to adversarial attacks, which can trick them into making glaringly incorrect decisions. Moreover, complex data-driven models typically lack interpretability or explainability, i.e., their decisions cannot be understood by human subjects. Furthermore, models are usually trained on standard datasets with a closed-world assumption. Hence, they struggle to generalize to unseen cases during inference in practical open-world environments, thus, raising the zero-or few-shot generalization problem. Although many conventional solutions exist, explicit domain knowledge, brain-inspired neural network and cognitive architectures offer powerful new dimensions towards alleviating these problems. Prior knowledge is represented in appropriate forms and incorporated in deep learning frameworks to improve performance. Brain-inspired cognition methods use computational models that mimic the human mind to enhance intelligent behavior in artificial agents and autonomous robots. Ultimately, these models achieve better explainability, higher adversarial robustness and data-efficient learning, and can, in turn, provide insights for cognitive science and neurosciencethat is, to deepen human understanding on how the brain works in general, and how it handles these problems.Index Terms-Domain knowledge, cognitive architecture, brain-inspired neural network, explainable AI, adversarial attack, zero-shot generalization. ✦ INTRODUCTION State of deep learningMachine learning is an artificial intelligence (AI) concept that enables computing systems to learn useful relationships from data and then use this information to identify learned patterns and make predictions on new inputs. Deep learning (DL) is a machine learning method that uses a multi-layered arrangement of computational units to learn relevant patterns from data. Many deep learning constructs have been proposed for various use-cases. Some of the most popular ones are deep convolutional neural networks (DCNN) [1], [2] recurrent neural networks (RNN) [3], generative adversarial networks (GANs) [4], deep reinforcement learning [5] and vision transformers [6], [7]). DCNNs are a class of deep neural networks (DNNs) specially designed to handle image data.Recently, deep learning methods have accomplished remarkable milestones in many critical applications. In medical domains, some state-of-the-art models (e.g., as reported

Cloud IoT-Oriented Neural Network-Based Taekwondo Teaching Scheme

“…It can be seen that in subfigures (1) to ( 4), the angle between the four students' left arm and the vertical direction is different, which is consistent with the quality evaluation grade the teacher gave. There are similar differences in the angles of the elbow and shoulder and vertical direction shown in subfigures ( 5) to (10), and the angles of legs and vertical direction and knees shown in subfigures ( 9) to (11). This shows that the proposed method can accurately recognize the different actions of Taekwondo Poomsae.…”

“…Equations ( 6) to (10) are the calculation of Kalman filtering. Equations ( 8) and ( 10) are used to ensure the recursion and persistence of the filtering.…”

“…It should be noted that the student's actions in the second column are not much different from those in the first column. Even though the pos-tures of some actions are more standard (such as subfigure (10)), the strength of the student's actions is insufficient. They do not exert force according to the training requirements, so the teachers consider them "sub-standard".…”

“…Recently, AI triggered by deep learning has swept many fields and achieved fruitful results [9][10][11]. In machine vision, natural language processing, and other fields, deep learning has demonstrated its advantages in processing big data and heterogeneous data from multiple sources.…”

Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches

“…These models employ a unique approach by simulating the generation process of occluded scenes, allowing them to distinguish between background context and targeted objects with a higher degree of accuracy [42]. Mathematically, generative algorithms aim to capture the intricate relationships between the observed data and the underlying occlusion patterns, enabling the synthesis of informative representations [43].…”